Smart Energy Controller for Household Load Management
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Solution Overview
Problem
Existing energy management systems in home environments fail to effectively reduce peak electrical grid loads and household electricity bills, particularly in regions with fluctuating energy tariffs and frequent power interruptions, such as South Africa.
Innovation Solution
A smart microprocessor and sensor-based controller system that selectively manages energy supply by switching high loads on or off based on external sensors' data, time, and tariff information, synchronized with solar intensity, allowing for remote control and integration of green energy sources to minimize grid load and optimize energy usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If existing energy management systems are used, then household energy usage is monitored, but peak electrical grid loads are not effectively reduced
Solution Approach 1:
The system performs preliminary actions by pre-cooling or pre-heating spaces and pre-charging batteries during off-peak hours before peak demand occurs. The controller proactively schedules high-energy tasks like water heating and appliance operation during low-tariff periods, preventing the need for intensive energy consumption during peak grid load times.
Solution Approach 2:
The system implements periodic action by cycling energy-intensive operations between off-peak and peak periods based on tariff schedules. High-load appliances are periodically switched on during low-tariff periods and off during high-tariff periods, creating a rhythmic load distribution pattern that reduces peak demand while maintaining operational requirements.
2Loss of energy
If existing energy management systems are used, then energy consumption is tracked, but household electricity bills are not significantly lowered
Solution Approach 1:
The system provides self-service by automatically interpreting tariff schedules, making load management decisions, and controlling appliances without manual user intervention. The controller autonomously optimizes energy consumption based on real-time tariff data and household patterns, eliminating the need for users to manually manage energy usage while achieving bill reduction.
Solution Approach 2:
The system implements feedback by continuously monitoring energy consumption, tariff rates, and load conditions, then using this information to dynamically adjust appliance operation. The controller receives feedback from sensors and tariff data, processes this information, and automatically modifies energy distribution to minimize costs while maintaining comfort and operational requirements.
3Loss of energy
If manual energy management is used, then users have control over appliances, but peak load reduction is ineffective
Solution Approach 1:
The system replaces manual mechanical control with automated electronic control. Instead of users physically switching appliances on and off, the controller uses electronic signals to automatically manage load distribution based on tariff data and grid conditions, substituting human decision-making with algorithmic optimization that responds instantaneously to changing conditions.
4Adaptability or versatility
If green energy sources are integrated, then renewable energy usage increases, but system complexity increases
Solution Approach 1:
The system achieves universality by designing a multi-functional controller that can manage multiple energy sources (grid electricity, solar panels, wind turbines, batteries) and multiple appliance types through a single integrated platform. The controller universally handles different energy inputs and load requirements using standardized communication protocols and control algorithms, reducing overall system complexity despite the diversity of components.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system reduces peak electrical grid loads, lowers household electricity bills, and enhances energy efficiency by intelligently managing energy distribution, especially during peak demand times, thereby alleviating power interruptions and improving national productivity.
Implementation Method 1
synchronized by the solar intensity as sensed by an external sensor, and uses the maximum average light intensity as incident during the day to synchronise and to control the energy switching or load shedding to individual loads in the household
Data Source
Figure 1
Figure 2
AI summary
The use of smart microprocessor and sensor based controller system that enables selective management of energy supply into a household of the invention is illustrated and consists of four sub-components i.e., a Grid Power Supply System A, A Power Demand System (B), a Specially Designed Power Distribution Box (C), a Smart Energy Controller (D), a Solar Energy Secondary Power Supply and Timing System Unit E, and a RF Remote Control Unit (G). Energy is supplied through the city network grid (A) to a specially designed power distribution box which redistributes power to separate lines through a series of power relay switches (C1), each serving a different load category e.g., heavy, medium, low, essential lighting, special load systems, requiring special phase requirements (C2 to C6). More than one load can be connected in parallel to a specific line C3 to C6. A second switch bank C2 is optional which is individually supplied by separate lines supplied, either directly from green energy supplies such as solar or thermal systems, or indirectly after storage in a battery or thermal energy storage system.